ForLiveSession2
Miguel Bonilla
08/01/2022
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###this is the rmarkdown file for “for live session 2” assignment
#load libraries to be used
library(ggplot2)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggthemes)#load the file onto R
cols = c("position" = "factor", "height" = "factor", "college" = "factor")
BBall <- read.csv("C:\\Users\\Migue\\Documents\\SMU\\Spring 2022\\6306 - Doing Data Science\\Unit2\\PlayersBBall.csv", header = TRUE, colClasses = c("position" = "factor", "height" = "numeric","college" = "factor", "weight" = "numeric"))
summary(BBall$position)## C C-F F F-C F-G G G-F
## 1 502 219 1290 388 216 1574 360#First part visually represent the number of players in each position
#easiest way to see number of players by position is a bar chart since these are classes
BBall %>% ggplot(aes(x = position)) + geom_bar(fill = "Darkslategray") +theme_stata() + ggtitle("Count of NBA Players by Position \n (1950-2018)")
#second part, visually investigate that the distribution of the weights of centers is greater than the weights of forwards
#start by setting up plot window to 2 columns using par(mfrow())
par(mfrow = c(2,1))
BBall[BBall$position == "C",] %>% ggplot(aes(x = weight)) + geom_histogram(fill = "darkslategray") +ggtitle("Weight Distribution for NBA Centers") + theme_stata()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 2 rows containing non-finite values (stat_bin).
BBall[BBall$position == "F",] %>% ggplot(aes(x = weight)) + geom_histogram(fill = "darkslategray") +ggtitle("Weight Distribution for NBA Forwards")+ theme_stata()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1 rows containing non-finite values (stat_bin).
#second attempt at showing the difference in distribution using a single plot instead of side by side (with a curve line instead of adjusting window)
BBall[BBall$position == "C" | BBall$position == "F",] %>% ggplot(aes(x = position, y = weight)) + geom_boxplot(fill = "darkslategray", color = "#4c9c9c") +theme_stata() + ggtitle("Weight Distributions for NBA Centers \n & Forwards")## Warning: Removed 3 rows containing non-finite values (stat_boxplot).
#third part visually investigate if height of C is higher than height of F
#did some data wrangling to clean up the table to change height formats from ft-in to feet in decimal form, ex. 6-3 -> 6.25
class(BBall$height)## [1] "numeric"BBall[BBall$position == "C" | BBall$position == "F",] %>% ggplot(aes(x = position, y = height)) + geom_boxplot(fill = "darkslategray", color = "#4c9c9c") + theme_stata()+ ggtitle("Height Distribution of NBA Centers \n & Forwards")
#fourth part, use dataset to visually investigate if the distribution of height is different between any of the positions
#not sure about this one, I would still think a boxplot comparing all the positions
BBall[BBall$position %in% c("C", "F", "C-F", "F-C", "G", "F-G", "G-F"),] %>% ggplot(aes(x = position, y = height)) + geom_boxplot(fill = "darkslategray", color = "#4c9c9c") + theme_stata() + ggtitle("Height Distribution of NBA Players \n by Position") 
#fifth section, visualizing how height changes by player’s weight
#how does x = weight explains y = height
BBall %>% ggplot(aes(x = weight, y = height)) + geom_point(position = "Jitter", aes(color = position)) + geom_smooth() + theme_stata() + scale_color_tableau() + ggtitle("Height (ft) vs. Weight (lbs) for NBA Players")## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
#section 6, is there a difference for the relationship between height and weight for different positions
BBall[BBall$position %in% c("C", "F", "C-F", "F-C", "G", "F-G", "G-F"),] %>% ggplot(aes(x = weight, y = height)) + geom_point(position = "jitter", aes(color = position)) + geom_smooth() +facet_wrap(~position) + theme_stata() + scale_color_tableau() + ggtitle("Height (ft) vs. Weight (lbs) for NBA Players")## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 5 rows containing non-finite values (stat_smooth).## Warning: Removed 5 rows containing missing values (geom_point).
#section 7 analyze the claim (visually) by historian that players heights have increased over the years
#to analyze this we will check a players starting year (as opposed to end year to account for players with shorter/longer careers)
#picking career start to control for players who might have joined at a younger/older age
BBall %>% ggplot(aes(x = year_start, y = height)) + geom_point(position = "jitter", aes(color = position)) + geom_smooth() + theme_stata() + scale_color_tableau() + ggtitle("NBA Players Height by Year of Career Start")## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
#section 8
library(plotly) #load plotly to build 3d plot##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutBBall[BBall$position %in% c("C", "F", "C-F", "F-C", "G", "F-G", "G-F"),] %>% plot_ly(x = ~height, y = ~weight, z = ~year_start, color = ~position) %>% add_markers() %>% layout(scene = list(xaxis = list(title = "Player Height (ft)"),
yaxis = list(title = "Player Weight (lbs)"),
zaxis = list(title = "Year of Career Start")),legend = list(title = list(text = "<b>Position<b>")))## Warning: Ignoring 5 observations#section 9
#picked animated bubble chart, to visualize trend over time
library(gganimate) #needed to analyze a trend over time
library(gifski) #needed to render the animation into gif
TopSchools = c("University of Kentucky", "University of California, Los Angeles", "University of North Carolina",
"University of Kansas", "Duke University","Indiana University", "University of Notre Dame")
schools = c("Duke","Indiana University", "UCLA", "Kansas", "Kentucky", "UNC", "Notre Dame")
p <- BBall[BBall$college %in% TopSchools & BBall$year_start > 1997,] %>%
ggplot(aes(x = college, fill = college)) +
geom_bar()+ scale_x_discrete(labels = schools) +
theme_stata()+ scale_fill_tableau() +
labs(title = 'Players Drafted into the NBA by School \n Year: {frame_time}') +
transition_time(year_start) +
enter_fade() +
enter_grow() +
ease_aes('sine-in-out')
animate(p, width = 826, height = 525, duration = 20)
#last part, separate dataset Visually test the claim that the distribution of incomes increase (mean or median) as the education level rises
library(plotly)
EdIncm <- read.csv("C:\\Users\\Migue\\Documents\\SMU\\Spring 2022\\6306 - Doing Data Science\\Unit2\\EducationIncome.csv", header = TRUE, colClasses = c( "Educ" = "factor"))
EdIncm## ï..Subject Educ Income2005
## 1 2 12 5500
## 2 6 16 65000
## 3 7 12 19000
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## 9 18 13-15 120000
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## 11 21 16 253043
## 12 22 16 45300
## 13 29 12 85000
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## 15 37 12 37000
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## 17 39 12 67000
## 18 41 13-15 120000
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## 2584 12140 13-15 45000EdSort = c("<12", "12", "13-15", "16", ">16") #order to re-sort x axis
summary(EdIncm$Educ)## <12 >16 12 13-15 16
## 136 374 1020 648 406E <- EdIncm %>% ggplot(aes(x = Educ, y = Income2005)) + geom_boxplot(color = "#4c9c9c", fill = "darkslategray") +scale_x_discrete(labels = EdSort) + scale_y_continuous(labels = scales::comma) + theme_stata() + stat_summary(fun = mean, color = "white", shape = 18) + ggtitle("Income for 2005 vs Educational Level")
E## Warning: Removed 5 rows containing missing values (geom_segment).
ggplotly(E)## Warning: 'box' objects don't have these attributes: 'mode'
## Valid attributes include:
## 'alignmentgroup', 'boxmean', 'boxpoints', 'customdata', 'customdatasrc', 'dx', 'dy', 'fillcolor', 'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hoveron', 'hovertemplate', 'hovertemplatesrc', 'hovertext', 'hovertextsrc', 'ids', 'idssrc', 'jitter', 'legendgroup', 'legendgrouptitle', 'legendrank', 'line', 'lowerfence', 'lowerfencesrc', 'marker', 'mean', 'meansrc', 'median', 'mediansrc', 'meta', 'metasrc', 'name', 'notched', 'notchspan', 'notchspansrc', 'notchwidth', 'offsetgroup', 'opacity', 'orientation', 'pointpos', 'q1', 'q1src', 'q3', 'q3src', 'quartilemethod', 'sd', 'sdsrc', 'selected', 'selectedpoints', 'showlegend', 'stream', 'text', 'textsrc', 'transforms', 'type', 'uid', 'uirevision', 'unselected', 'upperfence', 'upperfencesrc', 'visible', 'whiskerwidth', 'width', 'x', 'x0', 'xaxis', 'xcalendar', 'xhoverformat', 'xperiod', 'xperiod0', 'xperiodalignment', 'xsrc', 'y', 'y0', 'yaxis', 'ycalendar', 'yhoverformat', 'yperiod', 'yperiod0', 'yperiodalignment', 'ysrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'